Hira Meidia
Universitas Multimedia Nusantara

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Bershca: bringing chatbot into hotel industry in Indonesia Dennis Gunawan; Farica Perdana Putri; Hira Meidia
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14841

Abstract

Adopting technology could give competitive advantage and positively impact the hotel’s profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system’s front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system’s back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort.
Contour evolution method for precise boundary delineation of medical images Friska Natalia; Hira Meidia; Nunik Afriliana; Julio Christian Young; Sud Sudirman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14746

Abstract

Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.